The invention concerns a method and a device for runway localization on the basis of a feature analysis of at least one image of the runway surroundings taken by a landing aircraft.
For the localization of runways in images, one usually uses feature-based methods or template-based methods of image processing, see X. Gong, A.L. Abbott, “A Survey of Techniques for Detection and Tracking of Airport Runways”, 44th AIAA Aerospace Sciences Meeting and Exhibit, 9-12 January 2006, Reno, Nev. In these methods, features and templates of the specific visual components of the runways and possibly their surroundings such as boundary features, boundary markings, center lines, thresholds, runway identifiers, runway beacons or target markings are entered as model knowledge in feature and template databases. Through feature matching or template matching of the features or templates contained in the camera image with the model knowledge entered into the database, a resulting general situation of the runway can be determined in the image.
For example, in the domain of the feature-based methods, the dominant line features of the runway markings are often extracted and utilized by using so-called Hough transforms. The extracted line features are then compared with the model knowledge saved in the database as to the situation of runway boundaries, center lines or edges of thresholds and a general situation of the runway is determined in the image. The known feature and template-based methods which rely on the recognition and localization of specific visual components of the runway have various technical drawbacks, however.
On the one hand, the described feature and template matching involves a large computing expense in the aircraft. The high computing expense should be viewed especially against the background of the high demands on aviation-suitable computing hardware. These demands greatly restrict the available computing power in the aircraft—as compared to the computing power of commercial computing hardware. Furthermore, other disadvantages result from the need to use specific model knowledge. The recognition of specific visual components of the runway demands model knowledge available in the aircraft, for example stored in databases, on the visual components to be anticipated on different runways under different conditions. This model knowledge encompasses the specific appearances of the components, such as shapes and colors and their relative situations to each other. The model knowledge has to be checked continually for its validity and possibly adapted. This is necessary even without active structural changes to the runways. For example, over the course of time the center lines are increasingly covered over by tire abrasion in the area of the touchdown points of runways. The present visibility of individual components must therefore be current when included in the model knowledge. Since, furthermore, the model knowledge to be used is dependent on the time of day and the weather, a selection must be made of the presently used model knowledge in every landing approach. For example, models should be used at night or at twilight which allow for the visual features of the runway beacons. But these should also be used during the day if, for example, white markings can no longer be reliably recognized due to snowfall.
Even despite a selection of the model knowledge depending on the surroundings, additional hard to detect variations may occur in the appearances of the visual components, for example due to rain, fog, or cloud shadows. These visual variations can greatly affect the accuracy and reliability of the methods.
Therefore, traditional methods based on the recognition and localization of specific visual components of the runways require a time-consuming and costly creation, maintenance, availability and selection of model knowledge in the aircraft. Landing approaches to runways for which no model knowledge is available cannot be done in an image-assisted manner. Unavoidable variations in the appearances of the visual components can have great negative impact on the accuracy and reliability of the methods. Furthermore, the known methods involve a high demand for computing power, which requires an increased technical expense, especially in the airplane.